Registration of preoperative 3D data to 2D intraoperative fluoroscopy data has been widely proposed for a number of clinical applications. Systems for radiosurgery and neurosurgery are in widespread clinical use. These systems allow overlay of preoperative data onto interventional images or allow additional information from a preoperative Computerised Tomography (CT) scan (e.g. a radiotherapy plan) to be accurately aligned to the patient.
In more detail, prior to an operation a patient is typically subjected to a CT scan of the body area where the surgery will take place. This results in a three-dimensional image of the scanned body area. However, during surgery real time 2D fluoroscopy images are obtained of the same area, using for example a C-arm type fluoroscopy machine. However, a 2D fluoroscopy image may be insufficient to allow a surgeon to determine the precise position within the body of surgical instruments or surgical implants, particularly during catheter based MIS procedures. For example, during stent-graft repair of aortic aneurysms, precise stent placement is essential.
In order to address the drawbacks of the 2D images, it is known to augment the 2D real time image with the 3D pre-obtained image, obtained, for example from a CT scan. The problem then arises of ensuring accurate registration of the 3D image with the 2D image i.e. ensuring that the 2D image is aligned with the correct parts of the 3D image. FIG. 1 illustrates that CT position and orientation is defined by six rigid body parameters, being three translations X, Y, and Z, and three rotations θx, θy, and θz. These can be divided into parameters which define movements parallel to the plane of the fluoroscopy image (in plane parameters θx, Y, and Z), and parameters which define movements a component of which is normal to the fluoroscopy plane (out-of-plane parameters θy, and θz, and X). The registration problem is then one of how to manipulate these parameters such that the 3D data volume becomes aligned with the 2D image such that the surgeon can have some confidence in the registration achieved.
Various registration techniques are known in the art. Specifically, in Penney et al “An Image-Guided Surgery System to Aid Endovascular Treatment of Complex Aortic Aneurysms: Description and Initial Clinical Experience”, IPCAI 2011, LNCS 6689, pp. 13-24 the present inventors describe an intensity based registration technique which requires a starting position to be chosen by relying on visual inspection and identification of a vertebra in the fluoroscopy image. FIG. 3(a) to (c) illustrate the procedure, where from an initial position (FIG. 3(a)) a region of interest is drawn (FIG. 3(b)) using a GUI, and the chosen 3D CT vertebra surface is then manually translated over the fluoroscopy vertebra (FIG. 3(c)).
The problem with this arrangement is that accurate vertebra identification can be difficult, particularly when neither the thorax nor the pelvis are visible. In this respect, many vertebrae can look the same, and unless the medical technician performing the registration is able to accurately identify which vertebra is shown on the fluoroscopy image then no accurate registration will be possible. The overall effect of this problem is that the time taken to achieve registration is usually increased, whilst the medical technician attempts to identify which vertebrae can be seen in the fluoroscopy image.